Industrial and power generation turbines have control systems that monitor and control their operation. These control systems include control algorithms that can govern some or all operational aspects of the turbine.
Current control algorithms attempt to load (or unload) turbines, generators and various other components as may be applicable during load set point changes as fast as possible without violating the limits that facilitate a safe operation. However in such traditional systems and methods, the loading rates are typically limited by the structural constraints such as the highest stresses allowed in the rotor of a steam turbine to regulate life expenditure and other operational constraints such as clearance between rotating and non-rotating parts in the turbine. If the loading rates for various turbines are very high, large thermal gradients may develop in the turbines leading to high stresses and uneven thermal expansion that may result in contact or rubs between stationary and rotating parts. On the other hand, slow loading rates facilitate a safe operation but increase fuel costs and reduce plant availability. Because of an inability to accurately predict conditions within a plant, typical control methods use an unduly slow standard profile to facilitate safe operation. For instance, according to the measured metal temperatures at the beginning of the startup, the current controls may categorize the start-ups as hot, warm, or cold. Each of these start-up states uses loading rates slow enough to facilitate a safe operation for any startup in the same category. Consequently, such controlling methods may result in sub-optimal performance and higher operating costs.
One factor in the efficiency of a turbine such as, for example, a heavy-duty gas turbine, is the turbine clearance between the blade tips and the casing of the turbine. If the turbine clearance is maintained at a minimum level, the turbine will operate more efficiently because a minimum amount of air/exhaust gas will escape between the blade tips and the casing. Accordingly, a greater percentage of the air and gas entering the turbine will be used to drive the turbine blades and create work.
Due to the different thermal and mechanical growth characteristics of turbine rotor assemblies and the turbine casing, the turbine clearance may significantly change as the turbine transitions between different stages of operation such as from initial start-up to a base load steady-state condition. A clearance control system may be implemented in the turbine to address the turbine clearance conditions during the operation of the turbine. However, it may be advantageous to provide a control system that is able to dynamically monitor and predict turbine clearance conditions and component expansion, so as to allow for operating the turbine in its most efficient ranges.
Thus, there is a desire for systems and methods that provide for neural network based models to predict clearances in a turbine and for implementation in a control system to regulate clearances during its transient operation.
There is a further need for systems and methods for neural network modeling of turbine components.